{
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    {
      "cell_type": "code",
      "execution_count": null,
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        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "\n# Cross-validation on Digits Dataset Exercise\n\n\nA tutorial exercise using Cross-validation with an SVM on the Digits dataset.\n\nThis exercise is used in the `cv_generators_tut` part of the\n`model_selection_tut` section of the `stat_learn_tut_index`.\n\n"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "print(__doc__)\n\n\nimport numpy as np\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn import datasets, svm\n\ndigits = datasets.load_digits()\nX = digits.data\ny = digits.target\n\nsvc = svm.SVC(kernel='linear')\nC_s = np.logspace(-10, 0, 10)\n\nscores = list()\nscores_std = list()\nfor C in C_s:\n    svc.C = C\n    this_scores = cross_val_score(svc, X, y, cv=5, n_jobs=1)\n    scores.append(np.mean(this_scores))\n    scores_std.append(np.std(this_scores))\n\n# Do the plotting\nimport matplotlib.pyplot as plt\nplt.figure(1, figsize=(4, 3))\nplt.clf()\nplt.semilogx(C_s, scores)\nplt.semilogx(C_s, np.array(scores) + np.array(scores_std), 'b--')\nplt.semilogx(C_s, np.array(scores) - np.array(scores_std), 'b--')\nlocs, labels = plt.yticks()\nplt.yticks(locs, list(map(lambda x: \"%g\" % x, locs)))\nplt.ylabel('CV score')\nplt.xlabel('Parameter C')\nplt.ylim(0, 1.1)\nplt.show()"
      ]
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